The goal in few-shot WSOD is to learn a model that, given support images \(\mathcal {D}_{\rm train}\)  containing a set of novel classes \(\mathcal {L}\) , detects instances of the novel classes in query images \(\mathcal {D}_{\rm test}\) .
The support set consists of image-label pairs \((\mathbf {I}, \mathbf {y})\in \mathcal {D}_{\rm train}\)  where image-level label \(\mathbf {y}\subseteq \mathcal {L}\)  is a subset of classes present in the image \(\mathbf {I}\)In contrast to few-shot image classification, few-shot WSOD images can have multiple labels..
The support set is typically a small \(K\) -shot, \(N\) -way set sampled from a large dataset \(\mathcal {D}_{\rm novel}\)  with variety of novel classes \(\mathcal {C}_{\rm novel}\) .
The sampling process for few-shot WSOD follows
rules that are similar to few-shot classification problems [1]}, [2]}. A set of \(N\)  classes \(\mathcal {L}\subset \mathcal {C}_{\rm novel}\) , called
target classes, are first sampled.
Then, for each target class \(c \in \mathcal {L}\) , \(K\) 
images containing at least an instance of class \(c\)  are sampled without
replacement to create the support set \(\mathcal {D}_{\rm train}\) . The query set \(\mathcal {D}_{\rm test}\)  is sampled
similarly, but unlike the support set, query labels also contain bounding box
annotations in addition to the image-level labels as the goal is to detect
target objects in the query data. These bounding box annotations are only used for evaluation. Few-shot COL [3]}, [4]} is a special case of few-shot WSOD where there is
only one target class in the support set, i.e., \(N=1\) .
